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Social Science & Medicine 75 (2012) 2437e2445

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Social Science & Medicine

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Marginalized identities, discrimination burden, and mental health: Empiricalexploration of an interpersonal-level approach to modeling intersectionality

Julia S. Seng a,*, William D. Lopez b, Mickey Sperlich c, Lydia Hamama d, Caroline D. Reed Meldrume

aUniversity of Michigan, Institute for Research on Women and Gender, Ann Arbor, MI 48109, USAbUniversity of Michigan, School of Public Health, Ann Arbor, MI, USAcWayne State University School of Social Work and Merrill Palmer Skillman Institute, Detroit, MI, USAdUniversity of Michigan, Ann Arbor, MI, USAeWayne State University, Detroit, MI, USA

a r t i c l e i n f o

Article history:Available online 26 September 2012

Keywords:Health disparitiesDiscriminationIntersectionalityMethodsMental healthPosttraumatic stress disorderQuality of lifeUnited States

* Corresponding author.E-mail address: jseng@umich.edu (J.S. Seng).

0277-9536/$ e see front matter � 2012 Elsevier Ltd.http://dx.doi.org/10.1016/j.socscimed.2012.09.023

a b s t r a c t

Intersectionality is a term used to describe the intersecting effects of race, class, gender, and othermarginalizing characteristics that contribute to social identity and affect health. Adverse health effectsare thought to occur via social processes including discrimination and structural inequalities (i.e.,reduced opportunities for education and income). Although intersectionality has been well-describedconceptually, approaches to modeling it in quantitative studies of health outcomes are still emerging.Strategies to date have focused on modeling demographic characteristics as proxies for structuralinequality. Our objective was to extend these methodological efforts by modeling intersectionality acrossthree levels: structural, contextual, and interpersonal, consistent with a socialeecological framework. Weconducted a secondary analysis of a database that included two components of a widely used surveyinstrument, the Everyday Discrimination Scale. We operationalized a meso- or interpersonal-level ofintersectionality using two variables, the frequency score of discrimination experiences and the sum ofcharacteristics listed as reasons for these (i.e., the person’s race, ethnicity, gender, sexual orientation,nationality, religion, disability or pregnancy status, or physical appearance). We controlled for twostructural inequality factors (low education, poverty) and three contextual factors (high crime neigh-borhood, racial minority status, and trauma exposures). The outcome variables we modeled wereposttraumatic stress disorder symptoms and a quality of life index score. We used data from 619 womenwho completed the Everyday Discrimination Scale for a perinatal study in the U.S. state of Michigan.Statistical results indicated that the two interpersonal-level variables (i.e., number of marginalizedidentities, frequency of discrimination) explained 15% of variance in posttraumatic stress symptoms and13% of variance in quality of life scores, improving the predictive value of the models over those usingstructural inequality and contextual factors alone. This study’s results point to instrument developmentideas to improve the statistical modeling of intersectionality in health and social science research.

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Introduction

The negative health consequences of social inequality have beenwell established in the US (Carpiano, Link, & Phelan, 2008;Wilkinson,2005). However, attempts to explain this inequality that focus only ona single demographic factor, such as sex, race, or immigration status,often fall short of explaining health disparities. For example, suicideamong African American men is positively associated with educationand wealth (Burr, Hartman, & Matteson, 1999; Joe & Kaplan, 2001;Lester, 1991) presumably because of the interplay between race and

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a recent and tenuous position in themiddle class (Jackson &Williams,2006). Other authors have shown that while a college education isbeneficial, it differentially affectsmen andwomen (Meara, Richards, &Cutler, 2008) and that despite the presumed positive influence ofacculturation, immigranthealth statusworsenswith increasing lengthof US residence (Singh & Siahpush, 2002). The results of these studiesindicate that a more nuanced and complete consideration of one’ssocial identity should be incorporated into health outcomes research.

Intersectionality as a concept to improve health research

The concept of intersectionality can be used to explain howmultiple aspects of personal identity can impinge on health

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outcomes. Yet this conceptualization, which arises from the morehistorical, interpretive, or qualitative perspectives, lacks a compa-rably rich and nuanced statistical representation. The healthscience disciplines have yet to specify statistical approaches thatcan quantitatively mirror the rich theoretical articulation. There isa subjective quality to identity that cannot be captured in modelsthat consider demographic characteristics individually or withcumulative risk index variables. Crenshaw (1989) pointed to thiscomplex subjectivity:

[Black women] sometimes experience discrimination in wayssimilar to white women’s experiences; sometimes they sharevery similar experiences with Black men. Yet often they expe-rience double discriminationdthe combined effects of practiceswhich discriminate on the basis of race, and on the basis of sex.And sometimes, they experience discrimination as Blackwomendnot the sum of race and sex discrimination, but asBlack women (p. 149).

Although additive models of demographic characteristicsincorporate multiple jeopardy (King, 1988), they are not tailoredenough to capture particularities or unexpected effects of somecombinations of identities, such as the adverse effect of upper classstatus on Black men described above. This suggests that there aremultiple levels at which the effects of intersectionality mayimpinge on the individual and may be measurable, a considerationconsistent with multi-level, socialeecological models of humandevelopment (i.e., Bronfenbrenner, 1979) and of social determi-nants of health (e.g., Marmot & Wilkinson, 2006).

Accordingly, the conceptualization and operationalization ofintersectionality we propose here utilizes Bronfenbrenner’s model(1979) as an organizing framework. Bronfenbrenner positedthat individual psychological development takes place ininteractions within five concentric circles of mutually-influencing,socialeecological systems, moving from the intra-psychic to thefamilial and ever broader contexts. Although most often used asa psycho-social model, a person’s biology can be seen as part of theinnermost circle, making the theoretical framework applicable toresearch on health.

The state of the science in relation to statistical modeling ofintersectionality

Recent methodological papers by US and Canadian sociologistsreport complex statistical modeling approaches that potentiallyoffer better goodness-of-fit and more explained variance. Veenstra(2011) systematically considered four identities (race/ethnicity,gender, class, and sexual orientation) as additive main effects andthen also as interaction terms. He looked for “complicated direc-tionality” implied by other studies where the additive solution wasundermined. His results confirmed that interaction terms showedboth mitigating and aggravating contributions to health status. Theincrease in variance explained by the more complex variablesapproached one percent. Hinze, Lin, and Andersson (2011) alsoconducted a methodological study to “[provide] a bridge betweenfeminist frameworks and traditional quantitative studies of race,class, and gender effects” (p. 6). They combined stratification anda multiplicative approach, entering race and gender as interactionterms in a set of nine models stratified by socioeconomic status.Their results indicated that the interaction terms were indepen-dently predictive and that this association was strengthened whencontrolling for other factors, such as social support and riskbehaviors. The findings from the nine models’ coefficients affirmthat the answer to the research question changeswhen interactionswithin different groups are considered systematically. A method-ological study by Warner and Brown (2011) enriched the effort to

model intersectionality in twoways: first, they captured the impactof disadvantaged status across the life course via trajectories, andsecond, they considered race/ethnicity and gender jointly ina series of six dummy variables (e.g., White, Black, and MexicanAmerican men and women).

Each of these three approaches had strengths suited to theresearch questions used to illustrate the approach. These studiesbenefited from large samples well-suited to epidemiologicalquestions (from 3005 in the Hinze et al. study to 90,310 in theVeenstra study).

A socialeecological perspective

So far these approaches have relied on demographic items (e.g.,race, income, gender) and have emphasized structural inequalities.The above referenced authors have now all conceptually andstatistically linked intersectionality with structural inequality at thelevel of demographic variables. In reality, intersectionality may beplaying out across several levels. However, the conceptual linkagesand estimation of the contributions across several levels inmodeling have not yet been delineated (Bowleg, 2012). It makessense to carry this work forward, first by tentatively articulatingconceptual linkages between common variables and intersection-ality at other socialeecological levels and then estimating theproportion of variance in outcomes potentially attributable tointersectionality at each of these levels. Intersectionality might,conceptually and statistically, be a cross-cutting element that ismeasurable at all four levels of socialeecological conceptualizations.

There are many potential variables to operationalize this conceptwithin each level; these may vary depending on the research ques-tion but could be seen as variations on the definitions in Bronfen-brenner’s theory. Starting with Bronfenbrenner’s micro-level, wewould ideally measure the personal, internalized or intrapersonalvalence the person assigns to this identity, including potentiallymitigating effects of cherishing the identity (e.g., Mossakowski,2003). At a meso- or interpersonal level, exchanges involving expe-riences of discriminationor privilege could takeplace on a daily basis.It would not necessarily be carrying the identities themselves thatwould adversely affect health, but rather the discriminatory, aversive,stressful interpersonal exchanges that would potentially increase infrequency as the number of marginalized identities increased (e.g.,Kessler, Mickelson, & Williams, 1999). Thus these interpersonalexperiencesdwhich are amenable to changedwould be the media-tors between intersecting marginalized identities and healthdisparities. At the exo- or contextual level, ambient factors, such asliving as amember of a racial/ethnic or sexualminority or out-group,could impinge as additional stressors (e.g., Klest, 2012). Out-groupstatus could increase risk of victimization in terms of crime, threatsto civil liberties, or other identity-based trauma exposures (e.g., rape,hate crimes, arbitrary traffic stops, greater risk of incarceration,being forced to leave the family home in adolescence due to queeridentity). Finally, at the macro-level, structural inequalities ineducationand incomeassociatedwithmarginalized status could taketheir toll. It seems likely that modeling major demographic charac-teristics (i.e., race, gender, education, income) would explain a largeamount of variance if demographics were the only intersectionality-focused predictors. But more total variance might be explained ifmore levels could be taken into account. Considering several levelscould capture more particularity. A less partial, less distorted under-standing might emerge (Harding, 1991).

Extending the effort to model intersectionality

The recent engagement with the topic of intersectionality viamethods studies indicates a strong desire to bridge the abstract and

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the numerical. The purpose of this paper is to advance this meth-odological discussion of approaches to operationalizing inter-sectionality in quantitative health research. To that end, we focusedon three extensions of the above work. First, we delineated theconceptualization in a schematic manner consistent with an eco-social framework to foster systematic analyses within and acrossstudies, going from a conceptual definition, to listing empiricalindicators, to finding items in a health research study database tooperationalize these indicators (Table 1). Second, we shifted fromassessing statistical strategies applied to demographics alone toassessing statistical strategies applied to three levels. We focusedon the interpersonal level, taking contextual factors and structuralinequality factors into account in our models. Third, we useda database with a sample size more typical of clinical research(n ¼ 619).

To accomplish this focus on the interpersonal level, we con-ducted a secondary analysis of data from a study of posttraumaticstress disorder (PTSD) and childbearing outcomes that includeda measure of interpersonal experiences of discrimination. Thisinstrument, the Everyday Discrimination Scale (EDS) (Williams, Yu,Jackson, & Anderson, 1997) is an established survey instrumentused in a large number of health-related studies. The EDS has twocomponents: it is used to assess the burden of everyday experi-ences of discrimination and to list the potentially marginalizedidentities named as the reasons or attributions for these experi-ences. We refer to the variable quantifying the burden of discrim-ination as the EDS frequency score and to the variable indicating thenumber of potentially marginalized identities listed as the sum ofattributions. Although we expect both the number of marginalizedidentities and the burden of discrimination to be associated withmore adverse outcomes, we hypothesize that the relationship ofmarginalized identities with adverse outcomes will be mediated bydiscrimination.

The two outcome variables we used for this methods analysiswere scores on a quality of life index and symptoms of PTSD.Quality of life and PTSD are outcomes useful for this purposebecause both are predicted by factors at several of the eco-sociallevels. Quality of life ratings are derived from appraisal of satis-faction with health, partner and family relationships, job, housing,and neighborhood (Frisch, Cornell, Villanueva, & Retzlaff, 1992).PTSD occurs as a function of genetics, characteristics of the family oforigin, exposure to traumatic events, and risk varies by socialgroups (Koenen et al., 2009). For example, women are twice aslikely to develop PTSD as men (Breslau, 2001). African Americanwomen are notmore likely to develop PTSD but are much less likelyto recover from it (Seng, Kane Low, Sperlich, Ronis, & Liberzon,2011; Seng, Kohn-Wood, McPherson, & Sperlich, 2011). This maypotentially be due to the intersecting effects of disparate access tomental health treatment and disparate rates of on-going exposureto crime, accidents, and social network traumas, such as sudden,unexpected death or incarceration of a loved one. Sexual minorities

Table 1Delineation of the levels of conceptualizing intersectionality.

Conceptual definition Eco-social theory levels Emp

Intersectionality is a methodologyof studying "the relationshipsamong multiple dimensions andmodalities of social relationshipsand subject formations" (McCall, 2005).

Macro-system StruExo-system Con

Meso-system Inte

Micro-system Intra

a Not available in the database and so not modeled. EDS ¼ Everyday Discrimination S

have higher rates of PTSD (e.g., Roberts, Austin, Corliss,Vandermorris, & Koenen, 2010). Disabled people also have higherrates, often due to the accident or life-threatening condition thatcaused the disability (e.g., Zatzick et al., 2008) Thus both quality oflife and PTSD are particularly useful outcomes for methodologicalstudy of intersectionality, since modeling of multiple aspects ofidentity should, theoretically, explain a larger proportion ofvariance.

We proceeded through bivariate and multiple variable analysesto answer the following four research questions:

1. Is the sum of marginalized identities associated with greatermental health morbidity (i.e., PTSD) and decreased quality oflife?

2. Is this association mediated by the burden of discrimination?3. How important are contextual factors that might capture

meso- and exo-level effects of marginalized status experiencedvia differential rates of exposure to out-group status, crime, andtrauma?

4. To what extent do these multiple levels of intersectionalityexplain variance in mental health or quality of life beyond thatalready explained by structural inequalities (i.e., poverty, loweducation)?

Methods

This methodological study was a cross-sectional, secondaryanalysis of survey data collected as part of a prospective study ofthe effect of PTSD on childbearing outcomes in a diverse sample ofwomen expecting their first infant (United States National Insti-tutes of Health Grant R01 NR008767, PI Seng). The perinataloutcomes have been published (Seng, Kane Low, et al., 2011; Seng,Kohn-Wood, et al., 2011), and in-depth description of the recruit-ment and standardized computer-assisted telephone interviewprocedures have been presented elsewhere (Seng, Low, Sperlich,Ronis, & Liberzon, 2009). We summarized the most pertinentinformation here.

Procedures

Participants were recruited from 2005 through 2008 via eightmaternity clinics at three health systems, one in Ann Arbor, Mich-igan that serves predominantly privately insured patients and twoin Detroit, Michigan that serve predominantly publically insuredpatients (i.e., Medicaid recipients). The Institutional Review Boardsof the three health systems granted approval, including approvalfor use of verbal informed consent. A confidentiality certificate wasobtained from the National Institute of Mental Health. Eligibilitycriteria included being 18 years or older, expecting a first infant,being less than 28 weeks gestation, and able to speak English.Participants completed the screening interview early in their

irical indictors Operationalization in database

ctural inequality Low education, low incometextual factors Living as a member of a racial minority,

crime rate, and trauma exposuresrpersonal cost of the identity Burden of discrimination experiences

via the EDS frequency score and sumof attributions for discrimination outof nine identities

personal valence of the identitya Personal appraisal of the positive ornegative valence of identitiesa

cale.

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pregnancy (n ¼ 1581), and 1049 were enrolled for the longitudinalaspects of the study. Of these, 647 completed the late-gestation(35-week) interview, which contained the EDS items. Participantswere paid $20.

Measures

Ten variables were used in this analysis: race/ethnicity, educa-tion, income, residence in a high crime area, minority racial/ethnicstatus in relation to living in a postal code located in a Black-majority or White-majority city (i.e., Detroit or Ann Arbor), a sumof trauma exposures, PTSD symptom count, quality of life indexscore, EDS frequency score, and EDS sum of attributions fordiscrimination.

The Perinatal Risk Assessment Monitoring Survey (PRAMS), anepidemiological surveillance instrument created by the Centers forDisease Control and Prevention, was used to assess nominal-leveldemographic characteristics: income, education, and self-reported race/ethnic identity (Beck et al., 2002). We classifiedparticipants into six racial/ethnic groups: White, Black or AfricanAmerican, Asian or Pacific Islander, Native American or AlaskaNative, Hispanic, and Middle Eastern, an ethnicity we queried dueto the large ArabeAmerican community in the region. We classifiededucational attainment into less than or equal to high school versusgreater than high school. We defined poverty as annual householdincome less than or equal to $15,000. Participants were character-ized as living in a higher or lower crime area based on whether thetotal crime index number from the 2000 FBI uniform crime reportwas greater or less than the U.S. average in their residential zip code(SimplyMap, 2012).

A context-specific (i.e., city of residence) minority racial/ethnicstatus nominal variable was created based on the zip code in whichthe participant lived. The sample was geographically divided intoDetroit and Ann Arbor zip codes. According to U.S. census data(United States Census Bureau, 2012), the population of Ann Arbor is8.8% African American, and the population ofWashtenaw County inwhich Ann Arbor is located is 12.3% African American. Slightlymorethan 10% of the Black women in the study resided in the Ann Arborarea and thus were categorized as racial minorities at the contextlevel. The population of Detroit is 81.6% African American, and thepopulation of Wayne County (including suburbs) in which Detroitis located, is 40.9% African American; 50.4% of the residents ofWayne County are in racial/ethnic categories other than non-Hispanic and White. Slightly more than 7% of the White womenin the study resided in the Detroit area and thus were categorizedas racial minorities at the context level.

We measured lifetime trauma history with the Life StressorChecklist (LSC) (Norris & Hamblen, 2004; Wolfe & Kimerling, 1997),which assesses 29 types of trauma exposures. The LSC is designedfor use with women and asks about such potentially traumaticevents as disasters, war zone experience, medical trauma, incar-ceration, accidents, and abuse.We created an interval-level variablethat is the sum of types of trauma exposures reported by eachparticipant.

We measured symptoms of PTSD with the National Women’sStudy PTSD Module (NWS-PTSD). This standardized telephonediagnostic interview instrument attained a sensitivity of .99 andspecificity of .79 in diagnosing PTSD in comparison with the face-to-face, clinician-administered Structured Clinical Interview forDSM-III-R (SCID) in the DSM-IV field trial for PTSD (Resnick,Kilpatrick, Dansky, Saunders, & Best, 1993). This measure yieldsa symptom count (0e17) and diagnosis per DSM-IV symptomclusters. We used the interval-level symptom count. In this sample,the internal consistency coefficient for the symptom count (Cron-bach’s alpha) was .91.

We measured quality of life with the Quality of Life Inventory(QoLi) (Frisch et al., 1992), which assesses satisfaction with ninedomains including housing, health, love relationship, extendedfamily, community, job, leisure, health, and standard of living,providing an interval-level score. The internal consistency coeffi-cient in this sample was .79.

We used the nine-item Everyday Discrimination Scale (EDS)(Williams et al., 1997) to assess experiences of discrimination inrelation to multiple marginalized identities. The measure includesthe question, “How often on a day-to-day basis do you experienceeach of the following types of discrimination?” with responsesranging from never to almost everyday on a five-point scale. Itemsinclude experiences such as being made to feel inferior, receivingpoor service in a restaurant or store, being called names, and beinginsulted. We used the sum of these scores as an interval-levelindicator of burden of discrimination, with a higher score indi-cating that the participant experienced discrimination morefrequently in everyday life.

We then used the second component of the EDS to learn whichof several identities the participant considers to be the cause orreason for these experiences of discrimination. The measureincludes the question, “What do you think are the main reasons forthese experiences?” The list of identities to which the discrimina-tion can be attributed includes race, ethnicity/nationality, religion,sex, sexual orientation, disability, physical appearance, age, and/orsomething else (not specified). We added pregnancy status asanother possible attribution for purposes of our study. Thus, we usethe interval-level count of the number of memberships or identitiesthat a participant considered to be reasons for discrimination andconsider these identities as marginalizing because they generate(according to the participant) discrimination. The internal consis-tency (Cronbach’s alpha coefficient) for the 598 of 619 whoanswered all items was .86. Because reliability can differ acrossmore homogenous subgroups, we assessed Cronbach’s alphawithin the subgroup of Black women, where it was also .86.

Analysis plan

Analyses were conducted using SPSS version 17. Missing dataaffected one variable. Some individuals declined to answer indi-vidual items within the EDS frequency score section, and somewhoreported discrimination declined to name an identity as an attri-bution. These missing data affected the frequency score and thesample size, as explained below. Data were assessed for fit with theassumptions for linear modeling. The distributions for the twooutcome variables departed from normal; however, the standard-ized residuals of the regression models approached a normaldistribution. Thus, assumptions were not entirely met, but samplesize and the robustness of the procedure compensated for thesedeviations.

We then conducted the analyses in relation to our researchquestions. First, we described demographic characteristics,contextual factors, posttraumatic stress symptoms, and quality oflife scores for the race/ethnicity groups using chi squared analysisand t-tests to compare each group with all others. We thendescribed the experiences of discrimination using EDS frequencyscores and the sum of attributions, again contrasting each groupwith all others. We used two sets of regression models to estimatethe variance explained (via R2) of each intersectionality level(interpersonal, contextual, and structural inequalities) individuallyin relation to both the PTSD and quality of life outcomes. In theinterpersonal-level regressions we modeled alternatively the sumof attributions (an additive approach) and an interaction term (amultiplicative approach), following the methods of Veenstra (2011)and Hinze et al. (2011) respectively. Finally, we used two stepwise

J.S. Seng et al. / Social Science & Medicine 75 (2012) 2437e2445 2441

linear regressionmodels (one for PTSD and one for quality of life) toassess the relative contributions of the three levels of inter-sectionality factors within the same model. We started with theinterpersonal level, entering first the sum of attributions and thenthe EDS frequency score. This provided ameans to assess the extentto which the burden of discrimination experiences mediated theeffect of multiple marginalized identities, using Baron and Kenney’sprocedure for testing mediation (1986). We then continued addingsteps for the context level variables, adding the structuralinequality level variables as a last step.

Results

Sample

All 647 participants who completed the EDS answered itemsabout the frequency with which they experienced discrimination.For women who declined to answer or gave a don’t know responsefor individual items, we calculated their frequency scores from thenumber of items they did answer. Twenty-six womenwho reporteddiscrimination did not attribute the experience to any category ofmarginalized identity and were hence deleted from the analysisresulting in afinal sample size of 619. Slightlymore than half (55.3%)of the participants were White and one third (33.9%) were Black.

Structural inequality level factors

At the structural inequality level, White, Black, and Asian groupswere statistically significantly different, with Blacks consistentlymost disadvantaged in terms of both education and income (Table 2).

Contextual level factors

At the contextual level, the pattern was similar. Most womenhad racial identities congruent with the majority group in theircities, but 7.1% of White and 10.5% of Black women were in theminority where they lived. Eighty percent of the Black women livedin zip codes with crime rates greater than the U.S. average. Blackwomen experienced the greatest number of types of traumaexposure (mean of 5.6 out of 29 types of exposure). Compared withall other women, Black women had a significantly higher meannumber of PTSD symptoms (3.2, SD¼ 4.1, p< .001) and significantlylower Quality of Life scores (39.9, SD ¼ 5.0, p ¼ .003).

Interpersonal level factors

Table 3 presents mean EDS scores (with higher scores indicatinggreater burden of discrimination). The distribution is skewed to the

Table 2Descriptions by race and ethnicity group.a

White n ¼ 342(55.3%)

Black n ¼ 210(33.9%)

Asian orIslander

Structural inequality factorsHigh school education or less, % 14.9*** 78.1*** 6.4***Household income <$15,000, % 4.7*** 39.6*** 2.1**

Contextual factorsCrime rate > US average, % 5.3*** 80.5*** 8.5***Sum of trauma types, mean (SD) 3.5 (3.1) 5.6 (4.1) *** 2.7 (2.5)*

OutcomesPTSD symptoms, mean (SD) 1.1 (2.3) *** 3.2 (4.1) *** 68 (1.4) *Quality of life score, mean (SD) 41.3 (3.6) *** 39.9 (5.0) ** 40.1 (4.3

Note: **p < .01 ***p < .001. PTSD ¼ posttraumatic stress disorder. SD ¼ standard deviata Compares each group with all others.

left, with themode of 9, since one-third of the sample reported neverexperiencing discrimination. There were no statistically significantdifferencesacross groups ineither themean frequencyof experiencesof discrimination (ranging from a mean score of 13.5e15.8) or in therate of experiencing any discrimination (ranging from 62.9% to70.0%). The survey contains a logical skip pattern in that if thewoman reported no instance of discrimination, she was not askedto attribute discrimination to any of the nine possible marginalizedidentities. Therefore, we do not have reports of any identities thatare reasons for discrimination for roughly one third of the women.

Table 3 also presents ranking of attributions for the discrimi-nation experiences. Gender was the highest-ranked attribution inthe overall sample and for White and Hispanic women. Race wasthe highest-ranked attribution for Black and Asian/Pacific Islanderwomen. In this perinatal sample, participants also attributeddiscrimination due to their status as a pregnant woman, withWhites and Blacks ranking pregnancy as the third and fourth mostfrequent attribution, respectively.

Relationship of discrimination to outcomes

Greater burden of discrimination, as indicated by higher EDSfrequency scores, was negatively correlated with quality of life(r ¼ �.352, p < .001). The EDS frequency score was positivelycorrelated with PTSD symptom level (r ¼ .334, p < .001).

Predictive value of each intersectionality level in isolation

The first set of six regressions (Table 4) answers the question ofthe extent to which each level of intersectionality by itself ispredictive of PTSD symptoms or QoLi score. Each of the eightindividual variables is significant on its own (data not shown), butwhen combined into levels, the shared variance results in loss ofindependent significance for the sum of attributions, crime rate,and minority status variables. When each level of intersectionalityfactors is considered as a pair or trio of variables, all three levels aresignificantly predictive of both outcomes. The interpersonal levelalone explains 15% of variance in PTSD symptoms and 13% ofvariance in QoLi score. The contextual level alone explains 45% ofvariance in PTSD symptoms and 13% of variance in QoLi score. Thestructural inequalities level alone explains 5% and 4% respectively.

Assessment of interaction terms

Since both the Veenstra (2011) and Hinze et al. (2011)approaches to modeling intersectionality used interactions terms,we also tried this approach as an alternative to the additiveapproach implicit in the sum of attributions variable. The model

Pacificn ¼ 47 (7.6%)

Native American/AlaskaNative n ¼ 9 (1.5%)

Hispanicn ¼ 30 (4.8%)

Middle Easternn ¼ 18 (2.9%)

66.7 33.3 50.022.2 10.0 22.2

66.7 3.0 27.8** 6.6 (6.9) 4.3 (3.7) 5.6 (3.9)

** 5.9 (7.2) 1.7 (3.2) 3.4 (4.1)) 38.9 (8.8) 40.1 (4.0) 39.5 (4.0)

ion.

Table 3Everyday Discrimination Scale (EDS) scores and rates and ranking of reasons for discrimination by group.a

White n ¼ 342 (55.3%) Black n ¼ 210 (33.9%) Asian or Pacific Islander n ¼ 47 (7.6%) Hispanic n ¼ 30 (4.8%)

Mean discrimination frequency score (SD) 14.1 (5.4) 14.3 (6.9) 13.5 (6.0) 15.4 (6.2)Rate of any discrimination 67.5% 62.9% 63.8% 70.0%

Specific attribution ratesRace 3.5*** 43.6*** 58.1*** 52.4**Ethnicity/Nationality 3.5*** 21.1** 54.8*** 38.1**Religion 9.1 9.8 6.5 0Gender 65.8*** 42.1*** 45.2 57.1Sexual Orientation 1.7 5.3 0 4.8Disability 3.0 3.8 0 0Physical Appearance 16.9 21.8 16.1 19.0Age 35.5 38.3 25.8 47.6Pregnancy 22.9 33.1** 0*** 19.0Other Reason 26.0 21.8 12.9 4.8

Ranking1 Gender*** Race*** Race*** Gender2 Age Gender*** Eth/Nation *** Race**3 Pregnancy Age Gender Age4 Appearance Pregnancy** Age Eth/Nation **5 Religion Appearance Appearance Appearance6 Race*** Eth/Nation ** Pregnancy*** Pregnancy7 Eth/Nation *** Religion Sexuality8 Disability Sexuality9 Sexuality Disability

Note: Eth/Nation ¼ ethnicity or nationality, Sexuality ¼ sexual orientation. Empty cells indicate that this group had no other attributions for discrimination. **p < .01,***p < .001.

a Compares via chi squared test each group with all others. Samples sizes for Native American (n ¼ 9) and Middle Eastern (n ¼ 18) groups are too small to provide stableestimates and therefore are not presented. These participants are, however, included in the samples that contrasts with the White, Black, Asian/Pacific Islander, and Hispanicgroups.

J.S. Seng et al. / Social Science & Medicine 75 (2012) 2437e24452442

statistics provided as a footnote to Table 4 indicated that theinteraction product from multiplying EDS frequency score and thesum of attributions when used alone (per Veenstra) explain lessvariance in relation to both PTSD symptoms and QoLi score thanuse of the pair of EDS frequency score and sum of attributionsvariables. When the interaction product is used in addition to thesecomponent variables (per Hinze et al.), it does not increase varianceexplained in relation to PTSD. It does add 3.3% explained variance inrelation to QoLi score, but the interaction term itself is not inde-pendently predictive. Thus, for the final models, we used the twovariables without the interaction term.

Variance explained and mediating relationship

Finally, we constructed two stepwise linear regressions to seethe effects of modeling all three levels together (Table 5) and to

Table 4Linear regressions with each level’s intersectionality variable predicting outcomes.

Posttraumatic stress disorder (PTSD) symptom count

Interpersonal (Meso-system) levelModel statistics: R2 ¼ .151, F ¼ 54.6, p < .001Sum of attributionsa b ¼ .090 p ¼ .051Discrimination frequencya b ¼ .328 p < .001

Contextual (Exo-system) levelModel statistics: R2 ¼ .453, F ¼ 170.4, p < .001Crime rate b ¼ .018 p ¼ .564Minority status b ¼ .024 p ¼ .431Trauma exposures b ¼ .666 p < .001

Structural Inequalities (Macro-system) levelModel statistics: R2 ¼ .046, F ¼ 14.8, p < .001Low education b ¼ .139 p ¼ .001Poverty b ¼ .117 p ¼ .007

a When an interaction term is used instead, prediction does not improve; R2 ¼ .107, Ffrequency and sum variables, it does not add to the prediction of PTSD symptoms; R2 ¼

b When an interaction term is used instead, prediction does not improve; R2 ¼ .096, F ¼frequency and sum variables, it does add 3.3% to the prediction of the QoLi score, but tb ¼ �.096, coefficient p ¼ .388.

determine whether the burden of discrimination mediates theassociation of marginalized identities withmore adverse outcomes.In relation to the PTSD symptom outcome, the sum of attributionsvariable alone (Step 1) was significantly independently associatedand explained 8% of variance. In Step 2 the EDS frequency scoremediated the relationship of marginalized identities with PTSD (seefootnote to Table 5) and added 7% more explained variance. Addingthe contextual factors in Step 3 added 33%more explained variance.The structural inequalities variables, when added as the last step,were not significantly associated with PTSD and did not increaseexplained variance. In relation to the QoLi score outcome, thepattern was similar. The sum of attributions alone (Step 1)explained 7% variance in quality of life scores. The EDS frequencyscore again mediated this relationship and explained an additional6% of variance. The contextual factors added 6% more explainedvariance. Structural inequalities factors were independently

Quality of life index score

Model statistics: R2 ¼ .128, F ¼ 45.0, p < .001Sum of attributionsb b ¼ �.076 p ¼ .104Discrimination frequencyb b ¼ �.307 p < .001

Model statistics: R2 ¼ .128, F ¼ 30.2, p < .001Crime rate b ¼ �.041 p ¼ .288Minority status b ¼ �.009 p ¼ .808Trauma exposures b ¼ �.346 p < .001

Model statistics: R2 ¼ .042, F ¼ 13.6, p < .001Low education b ¼ �.108 p ¼ .012Poverty b ¼ �.137 p ¼ .002

¼ 74.3, interaction term b ¼ .328, model p < .001. When it is used in addition to the.151, F ¼ 36.3, interaction term b ¼ .006, coefficient p ¼ .960.65.5, interaction term b ¼ �.310, model p < .001. When it is used in addition to the

he independent association is not significant; R2 ¼ .129, F ¼ 30.3, interaction term

Table 5Step-wise regression models of effect of levels of intersectionality on the outcomes, with test of mediation.

PTSD symptom level Quality of life score

Cost of attributions, Step 1Variance explained, Model sig, R2 ¼ .081, p < .001 R2 ¼ .066, p < .001Sum of attributionsa b ¼ .284 p < .001 b ¼ �.257 p <.001

Frequency of discrimination, Step 2Variance explained, Model sig, DR2 ¼ .070, R2 ¼ .151, p < .001 DR2 ¼ .061, R2 ¼ .128, p < .001

b p b pSum of attributionsa .090 .051 �.076 .104EDS frequency scorea .328 <.001 �.307 <.001

Contextual factors, Step 3Variance explained, Model sig, DR2 ¼ .327, R2 ¼ .478, p < .001 DR2 ¼ .064, R2 ¼ .192, p < .001

b p b pSum of attributions .037 .314 �.050 .268EDS frequency score .141 <.001 �.240 <.001Crime rate .036 .238 .070 .061Minority status .009 .758 �.015 .698Trauma exposures .603 <.001 �.244 <.001

Structural inequalities factors, Step 4Variance explained, Model sig, DR2 ¼ .002, R2 ¼ .480, p < .308 DR2 ¼ .014, R2 ¼ .206, p¼.004

b p b pSum of attributions .037 .307 �.051 .256EDS frequency score .144 <.001 �.239 <.001Crime rate .009 .805 .016 .734Minority status .008 .785 .019 .598Trauma exposures .597 <.001 �.225 <.001Low education .012 .751 �.077 .097Poverty .047 .157 �.102 .013

Note: PTSD ¼ posttraumatic stress disorder.a Step 1 tests the predictor to outcome (A to C) relationship per Baron and Kenney’s procedure for testing mediation (1986). The predictor to mediator relationship (A to B),

which is the same across models, was tested with a bivariate regression of sum of attributions/identities and the dependent variable of the burden of discrimination score,R2 ¼ .350, Model p < 001, b ¼ .591, coefficient p < .001. The mediator to outcome test (B to C) is the last coefficient in the second step. We conclude that burden ofdiscrimination score mediates the association of sum of attributions/identities with PTSD symptoms and quality of life score.

J.S. Seng et al. / Social Science & Medicine 75 (2012) 2437e2445 2443

significantly associated with the QoLi score, even when added last,explaining an additional 1% of variance. Altogether the three levelsof intersectionality factors explained 48% of variance in relation toPTSD symptoms and 21% of variance in relation to QoLi score.

Discussion

Our objective was to contribute to the dialog in the healthsciences literature on how to operationalize the concept of inter-sectionality. The contributions we aimed to make via thissecondary analysis were (1) to suggest that intersectionality isa construct that could be examined at four levels, mirroring thelevels of Bronfenbrenner’s socialeecological model: structural,contextual, interpersonal, intrapersonal; (2) to focus on anapproach that utilizes an instrument already in widespread use tomeasure and model the interpersonal-level indicator; and (3) toconsider the relative amount of variance explained by these threelevels within a medium-sized sample from a clinical study.

From models of each level alone, we concluded that theinterpersonal-level intersectionality variables explain more vari-ance in relation to the mental health and quality of life outcomesthan structural inequality-level intersectionality variables. Thevariable created from the sum of marginalized social identities isindependently predictive of our two outcomes. This associationwasmediated by a second interpersonal-level variable, the frequencyscore reflecting the burden of everyday discrimination experiences.

The exploration of contextual level factors showed that aggre-gated variables (e.g., minority or out-group status, crime rate by zipcode) may have less impact than individually specific indictors,such as individually experienced trauma exposures. Trauma expo-sure is an example of a variable used to operationalize thecontextual level of intersectionality that is probably study-specific

because it is a necessary antecedent to the outcome of PTSD. Thisis a logical explanation of the strong independent association(b¼ .666) and high proportion of variance explained by this level inthe PTSD model (45%). But the contextual level variables alsoexplained 13% of the variance in quality of life, where traumaexposure (b ¼ �.346) makes sense as a contextual factor but is nota necessary predictor.

Appraisal of this approach

Results of this analysis permit appraisal from a statisticalperspective.We suggested that operationalizing intersectionality inquantitative health outcomes research is difficult because it isa social phenomenon operating at multiple levels. The approach weused here falls short of the ideal largely because the keyinterpersonal-level variable is mathematically simplistic, relying ona sum of identities that were named as attributions for discrimi-nation. Additive, multiplicative, or combination variables that comeinto play at the analysis phase of a research project and that applythe same mathematical or statistical procedure for all individualswill not be as finely specific as variables that could capture positiveand negative valences and relativeweights of identities. These likelyare factors that could be better captured at the data collection phasewith yet-to-be-created instruments aiming to measure the intra-personal level. For example, a lesbian whose family of originembraces her partner might experience much less negative impactof her identity compared to a lesbian estranged from her family.A Black woman with a strong African American cultural heritagemay be more resilient than some of her counterparts. From thisstudy we cannot know if such intrapersonal-level factors areindependently significant predictors. Nor can we know if theywould explain additional variance.

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Our analysis of interaction terms to capture the intersection ofidentities and discrimination was an attempt to capture thispotential individuality of effects of discrimination. The interactionterms did not improve the models. Ultimately the product of thediscrimination frequency score and the sum of attributions (iden-tities) is still based on an additive approach because the data werelimited to the sum of identities causing discrimination. We do nothave any numerical representation of identities that are protective,privileging, or cherished, neither as a simple subtractionwithin theinterpersonal level nor via a system of weighting the identitiesrelative to each other. Thus we recommend that future analysescontinue to assess the benefits of using interaction terms, especiallyif and when valence and weight information is available for thecalculations.

Modeling three of the four posited levels at which inter-sectionality may be operating may compensate to some extent forthe simple, additive approach because the multiple regressionallows each factor to influence the strength of association with theoutcome of each of the others. We chose to enter the structuralinequality factors last in the two final models becausewewanted toestimate the importance of the novel interpersonal-level factorsalone and then to apply a test of mediation. The models likelyunderestimate the importance of the interpersonal level of inter-sectionality for two reasons. First, the sum of attributions onlycaptures the negative valence (i.e., marginalizing identities).Second, the one-third participants who reported no discriminationcontributed little variance, since theoretically measurable experi-ences of protection or privilege do not enter into this equation.Although these models allow us to see the relative contributions ofeach level to explaining variance in the outcomes, the databaseused included only pregnant women, so there was less variancethan would be expected in studies including more variance ingender and age.

For a more qualitative appraisal of this effort, we turn to threequestions suggested by Cole (2009) for improving consideration ofintersectionality in research. First, we ask, “Who is included” in thedata set? Although the sample we used is diverse, it is a limitationof this secondary analysis approach that we did not ask everyparticipant about identities, regardless of whether they also re-ported discrimination. This could be easily amended in future datacollection. Second, we ask, “What similarities exist within thesample?” Female gender appears to be an “equal opportunity” riskfactor for discrimination. Gender-based problems (e.g., sexualtrauma) and gender-specific variance (e.g., higher conditional PTSDrisk for women) affected this whole sample equally. So focusing ona third question, “What role do inequalities play?” would likely befruitful and indicate areas amongwomenwhere race, class, or otheridentities lead to differential outcomes (e.g., access to mentalhealth treatment to improve PTSD symptoms or opportunities tomove to safer neighborhoods to improve quality of life).

Our effort to operationalize intersectionality is offered in thespirit of continuing a dialog and a dialectical process among healthoutcomes researchers to move toward a less partial, less distorted(Harding, 1991) estimation of the adverse impact of socialinequalities on health across multiple socialeecological levels,including the macro level where their impact has so far been mostthoroughly studied. Several of the shortcomings of this analysiscould be easily addressed with minimal effort at the data collectionphase by augmenting the initial demographic assessment toinclude more questions about belonging to marginalized minoritygroups and other social identities of theoretical interest to thestudy. A next step would be to ask participants to indicate therelative positive or negative valence of each identity and to weighor rank the perceived importance of each identity relative to theothers. The addition of attention to contextual, interpersonal-level,

and intrapersonal-level variables could inform clinical and epide-miological research. Other ideas that emerge acrossmethodologicaldiscussions may significantly increase our ability to explain theimpact of particular social standpoints or our ability to understandwhy some individuals fare so much worse than others who on thesurface seem similar. Ultimately the goal of producing suchknowledgewould be to affect social change and improve the healthof those who experience marginalization at many levels.

Acknowledgments

This study was funded by the National Institutes of Health,National Institute for Nursing Research grant number NR008767(Seng, PI), “Psychobiology of PTSD & Adverse Outcomes of Child-bearing.” The content is solely the responsibility of the authors anddoes not necessarily represent the official views of the NationalInstitute of Nursing Research or the National Institutes of Health.

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